Category: Data

We had an in-person meeting at the MTG during the MetaBrainz summit to discuss the status and future of AcousticBrainz. We came up with a rough outline of things that we want to work on over the next year or so. This is a small list of tasks that we think will have a good impact on the image of AcousticBrainz and encourage people to use our data more.

State of AcousticBrainz

AcousticBrainz has a huge database of submissions (over 10 million now, thanks everyone!), but we are currently not using the wealth of data to our advantage. For the last year we’ve not had a core developer from MetaBrainz or MTG working on existing or new features in AcousticBrainz. However, we now have:

Philip, who is starting a PhD at MTG, focused on some of the algorithms/data going into AcousticBrainz

Alastair, who now has more time to put towards management of the project

Because of this, we’re glad to present an outline of our next tasks for AcousticBrainz:

Short-term

Some small tasks that are quick to finish and we can use to show off uses of the data in AcousticBrainz

Merge Philip’s similarity, including an API endpoint

Philip’s masters thesis project from last year uses PostgreSQL search to find acoustically similar recordings to a target recording. This uses the features in AcousticBrainz. We need to ensure that PostgreSQL can handle the scale of data that we have.

An extension of this work is to use the similarity to allow us to remove bad duplicate submissions (we can take all recordings with the same MBID and see if they are similar to each other, if one is not similar we can assume that it’s not actually the same as the other duplicates, and mark it as bad). We want to make these results available via an API too, so that others can check this information as well.

Merge Existing PRs

We have many great PRs from various people which Alastair didn’t merge over the last year. We’re going to spend some time getting these patches merged to show that we’re open to contributions!

Publish our Existing models

In research at MTG we’ve come up with a few more detailed genre models based on tag/genre data that we’ve collected from a number of sources. We believe that these models can be more useful that the current genre models that we have. The AcousticBrainz infrastructure supports adding new models easily, so we should spend some time integrating these. There are a few tasks that need to be done to make sure that these work

Ensure that high-level dumps will dump this new data (If we have an existing high-level dump we need to make a new one including the new data)

Ensure that we compute high-level data for all old submissions (we currently don’t have a system to go back and compute high-level data for old submissions with a new model, the high-level extractor has to be improved to support this)

Update/fix some pages

We have a number of issues reported about unclear text on some pages and grammar that we can improve. Especially important are

Front page (Show off what we have in the project in more detail, instead of just a wall of text)

Data page (instead of just showing tables of data, try and work out a better way of presenting the information that we have)

Fix Picard plugin

When AB was down during our migration we were serving HTML from our API pages, which caused Picard to crash if the AB plugin was enabled while trying to get AB data. This should be an easy fix in the Picard plugin.

High Impact

These are tasks that we want to complete first, that we know will have a high impact on the quality of the data that we produce.

Frame-level data

We want to extract and store more detailed information about our recordings. This relies on working being done in MTG to develop a new extractor to allow us to get more detailed information. It will also give us other improvements to data that we have in AB that we know is bad. This data is much bigger than our current data when stored in JSON (hundreds of times larger), so we need to develop a more efficient way of storing submissions. This could involve storing the data in a well-known binary data exchange format. A bunch of subtasks for this project:

Finish the essentia extractor software

Decide on how to store items on the server (file format, store on disk instead of database)

Work out a way to deal with features from two versions of the extractor (do we keep accepting old data? What happens if someone requests data for a recording for which we have the old extractor data but not the new one?)

Upgrade clients to support this (Change to HTTPS, change to the new API URL structure, ensure that clients check before submission if they’re the latest version, work out how to compress data or perform a duplicate check before submission)

Deduplication (If we have much larger data files, don’t bother storing 200 copies for a single Beatles song if we find that we already have 5-10 submissions that are all the same)

MusicBrainz Metadata

Rashi’s GSoC project in 2018 helped us to replicate parts of the MusicBrainz database into AcousticBrainz. This allows us to do amazing things like keep up-to-date information about MBID redirects, and do search/browse/filtering of data based on relationships such as Artists just by making a simple database query. We want to merge this work and start using it.

Dumps

When we changed the database architecture of AcousticBrainz in 2015 we stopped making data dumps, making people rely on using the API to retrieve data. This is not scalable, and many people have asked for this data. We want to fix all of the outstanding issues that we’ve found in the current dumps system and start producing periodic dumps for people to download.

Build more models

In addition to the existing models that we’ve already built (see above, “Publish our Existing models”), we have been collecting a lot of metadata that we could use to make even more high-level models which we think will have a value in the community. Build these models and publicly release them, using our current machine learning framework.

Wishlist

These are tasks that we want to complete that will show off the data that we have in AcousticBrainz and allow us to do more things with the data, but should come after the high-impact tasks.

Expose AB data on MusicBrainz

As part of the process to cross-pollinate the brainz’s, we want to be able to show a small subset of AB data that we trust on the MB website. This could include information such as BPM, Key, and results from some of our high-level models.

Improve music playback

On the detail page for recordings we currently have a simple YouTube player which tries to find a recording by doing text search. We want to improve the reliability and functionality of this player to include other playback services and take advantage of metadata that we already have in the MusicBrainz database.

Scikit-learn models

The future of machine learning is moving towards deep learning, and our current high-level infrastructure written in the custom Gaia project by MTG is preventing us from integrating improved machine learning algorithms to the data that we have. We would like to rewrite the training/evaluation process using scikit-learn, which is a well known Python library for general machine learning tasks. This will make it easier for us to take advantage of improvements in machine learning, and also make our environment more approachable to people outside the MusicBrainz community.

Dataset editor improvements

Part of the high-level/machine learning process involves making datasets that can be used to train models. We have a basic tool for building datasets, however it is difficult to use for making large datasets. We should look into ways of making this tool more useful for people who want to contribute datasets to AcousticBrainz.

Search

With the integration of the MusicBrainz database into AcousticBrainz, we will be able to let people search for metadata related to items which we know only exist in AcousticBrainz. We think that this is a good way for people to explore the data, and also for people to make new datasets (see above). We also want to provide a way that lets people search for feature data in the database (e.g. “all recordings in the key of Am, between 100 and 110BPM”).

API updates

As part of the 2018 MetaBrainz summit we decided to unify the structure of the APIs, including root path and versioning. We should make AcousticBrainz follow this common plan, while also supporting clients who still access the current API.

We should become more in-line with the MetaBrainz policy of API access, including user-agent reporting, rate limiting, and API key use.

Request specific data

Many services who use the API only need a very small bit of information from a specific recording, and so it’s often not efficient to return the entire low-level or high-level JSON document. It would be nice for clients to be able to request a specific field(s) for a recording. This ties in with the “Expose AcousticBrainz data on MusicBrainz” task above.

Everything else

Fix all our bugs and make AcousticBrainz an amazing open tool for MIR research.

Thanks for reading! If you have any ideas or requests for us to work on next please leave a comment here or on the forums.

A new virtual machine for running your own mirror of MusicBrainz is now available. Thanks to InvisibleMan78 for the continuous feedback and to Jeff Sturgis for his work with Docker Compose! This is the last virtual machine to include the soon defunct search server. Links:

This server release converts more search results pages to React and fixes seven bugs. Thanks to @riipah for fixing @TouhouDB handler. Search scores are no longer displayed on search results page for clarity as they misleadingly weighed down the the importance of results appearing on the first page but not in the first place. The git tag is v-2018-08-14.

I have recently released a new MusicBrainz virtual machine. This virtual machine includes all the important bits of MusicBrainz so you can run your own copy! I’d been hoping for feedback if people have encountered any problems with this VM, but I’ve not received any feedback. Here is to hoping that no news is good news!

I’m pleased to report that our nightmare of finding/reconstructing the missing replication packets is finally over!

Through many heroic hours of work, Bitmap and Chirlu have reconstructed the missing replication packets. All clients should now be on their way to being up to date. We’ve learned a number of lessons (some good, some bad — that’s life, right?) in this ordeal and we hope to avoid these issues in the future.

An integral part of this recovery process were a number of people from our community who helped us: Users mbcz, rembo10 and xeam sent us their complete DB dumps! Bitmap used these to sanity check and diff several other database to finally extract the missing packets. Thank you for dropping what you were doing and sending us a few GB of data over blazingly fast connections. Without you this would not have been possible; and this is not an exaggeration. Thank you!

After some more rest we’re going to continue to put out smaller fires that remain from the move to NewHost, but for now, the big fires are put out. Just in time for the weekend!

In the 11 year history of the replication stream we’ve had to have users restart their stream about 3-4 times because of problems on our end. Zero would’ve been nicer, but I’m proud that we’ve been able to make this system work for so long. On a daily basis we seem to have about 400 replicated copies of MusicBrainz running all over the world. Clearly this part of our service is well used and I sleep a little better at night knowing that our most critical data is backed up across the globe.

Just for fun, here is a graph of the replication API usage over the last 6 months:

Towards the end the graph shows the week plus long break, then a small blip as some of our replicas got unstuck yesterday and the much larger spike shows the rest of the replicas getting unstuck. Now, as to what caused the blip in mid-October — I have no idea.

There is a new test VM available for anyone who would like to try the latest, possibly not fully debugged, VM. I’m not sure why the VM is nearly 20GB larger than the previous one, while containing roughly the same stuff, but that is what we’re stuck with for this test. I’ll try harder to minimize the size for the final build.

IMPORTANT: Please ignore the usage tips published on the wiki — they do not apply to this release. For the next release I’ll try and match more of the characteristics of the last version. Do read the usage tips above!